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118 results for “spatial prediction”
Data from: Spatial scale matters for predicting plant invasions along roads
<p>Biological invasions threaten global biodiversity and can have severe economic and social impacts. The complexity of this problem challenges effective management of invasive alien species as the contribution of many factors involved in the invasion processes across different spatial scales is not well understood.</p> <p>Here, we identify the most important determinants associated with the occurrence of two invasive alien plants, the North American goldenrods (<em>Solidago canadensis</em> and <em>S. gigantea</em>), commonly found in agricultural landscapes of Europe. We used Google Street View images to perform a remote, large-scale inventory of goldenrods along 1,347 roadside transects across Poland. Using open access geospatial data and machine learning techniques, we investigated the relative role of nearly 50 variables potentially affecting the distribution of studied species at five spatial scales (from within 0.25 km to 5 km of the studied locations).</p> <p>We found that the occurrence of goldenrods along roadsides was simultaneously associated with multiple drivers among which those related to human impacts, climate, soil properties and landscape structure were the most important, while local characteristics, such as road parameters or the presence of other alien plants were less influential. However, the relative contribution of different variables in predicting goldenrod distribution changed across spatial scales.</p> <p><em>Synthesis</em>:<em> </em>Mechanisms underlying plant invasions are highly complex and a number of factors can jointly influence the outcomes of this process. However, since different invasion drivers operate at different spatial scales, some important associations may be overlooked when focusing on a single spatial context. Although associations were consistent in direction (positive or negative) across scales, their relative influence on goldenrod occurrence often changed. Socio-economic factors were largely important at local scales, while the effect of landscape factors broadly increased with increasing spatial scale. We highlight that using multi-scale approaches involving a wide range of variables may enable setting priorities for the management of invasive alien plants.</p>
Biogeography of the world's worst invasive species has spatially-biased knowledge gaps but is predictable
<p>The world's "100 worst invasive species" were listed in 2000. The list is taxonomically diverse and often cited (typically for single-species studies), and its species are frequently reported in global biodiversity databases. We acted on the principle that these notorious species should be well-reported to help answer two questions about global biogeography of invasive species (i.e., not just their invaded ranges): (1) "how are data distributed globally?" and (2) "what predicts diversity?" We collected location data for each of the 100 species from multiple databases; 95 had sufficient data for analyses. For question (1), we mapped global species richness and cumulative occurrences since 2000 in (0.5 degree)<sup>2</sup> grids. For question (2) we compared alternative regression models representing non-exclusive hypotheses for geography (i.e., spatial autocorrelation), sampling effort, climate, and anthropocentric effects.</p> <p>Reported locations of the invasive species were spatially-biased, leaving large gaps on multiple continents. Accordingly, species richness was best explained by both anthropocentric effects not often used in biogeographic models (Government Effectiveness, Voice & Accountability, human population size) and typical natural factors (climate, geography; R<sup>2</sup> = 0.87). Cumulative occurrence was strongly related to anthropocentric effects (R<sup>2</sup> = 0.62). We extract five lessons for invasive species biogeography; foremost is the importance of anthropocentric measures for understanding invasive species diversity patterns and large lacunae in their known global distributions. Despite those knowledge gaps, advanced models here predict well the biogeography of the world's worst invasive species for much of the world.</p>
Data from: Accounting for uncertainty in marine ecosystem service predictions for spatial prioritisation
<p>Spatial assessments of Ecosystem Services (ES) are increasingly used in environmental management and spatial planning, but rarely provide information on the accuracy of predictions. Uncertainty estimates are essential to allow for confidence in the quality and credibility of ES assessments to enable informed decision-making. In marine environments, the need for uncertainty assessments for ES is unparalleled as they are data scarce, poorly (spatially) defined, with complex interconnectivity of seascapes. This study illustrates the uncertainty associated with a principle-based method for ES modelling by accounting for model variability, data coverage, and uncertainty in thresholds and parameters. A sensitivity analysis was applied on ES models for marine bivalves (<em>Austrovenus stutchburyi</em> and <em>Paphies australis</em>) and their contribution to <em>Food provision, Water quality regulation, Nitrogen removal,</em> and <em>Sediment stabilisation</em>.<em> </em>ES estimates from the sensitivity analysis were compared against baseline ES predictions. Spatial uncertainty patterns were analysed for individual ES through bi-plots and multiple ES through spatial prioritisation using Zonation. Results showed spatially explicit differences in uncertainty patterns for ES and between species. <em>Food</em><em> provision</em> had highest maximum uncertainty (>5 points) but also the largest area of high ES and high certainty conditions. Zonation analysis conducted on baseline and conservative ES values showed overall robust outcomes of top 30% area, but important nuances through shifts in top 10% and top 5% area that allowed for a consistently better representation of ES when accounting for uncertainty. The spatial prioritisation in combination with the ES uncertainty biplots provide tools for spatial planning of individual and multiple ES to focus on area of highest value with highest certainty and can thereby help reduce risk and aid informed decision-making at acceptable confidence levels. This type of information is urgently needed in marine ES assessments and their management, but likewise extends to other environments to improve transparency. </p>
Data and scripts for: Airborne DNA reveals predictable spatial and seasonal dynamics of fungi
<p><span>Fungi are among the most diverse and ecologically important kingdoms of life. However, the distributional ranges of fungi remain largely unknown, as do the ecological mechanisms that shape their distributions. To provide an integrated view of the spatial and seasonal dynamics of fungi, we implemented a globally distributed standardised aerial sampling of fungal spores. The vast majority of OTUs were detected only within one climatic zone, and the spatio-temporal patterns of species richness and community composition were mostly explained by annual mean air temperature. Tropical regions hosted the highest fungal diversity except for lichenized, ericoid mycorrhizal, and ectomycorrhizal fungi, which reached their peak diversity in temperate regions. The sensitivity in climatic responses was associated with phylogenetic relatedness, suggesting that large-scale distributions of some fungal groups are partially constrained by their ancestral niche. There was a strong phylogenetic signal in seasonal sensitivity, suggesting that some groups of fungi have retained their ancestral trait of sporulating only for a short period. Overall, our results show that the hyperdiverse kingdom of fungi follows globally highly predictable spatial and temporal dynamics, with seasonality in both species richness and community composition increasing with latitude. Our study reports patterns resembling those described for other major groups of organisms, thus making a major contribution to the long-standing debate on whether organisms with microbial lifestyles follow the global biodiversity paradigms known for macro-organisms.</span></p> <p>The analyses presented in the paper can be reproduced with the R-script pipeline provided here. The starting point for the scripts is the datafile allData.RData that was published originally by Ovaskainen et al. Data from: Global Spore Sampling Project: A global standardized dataset of airborne fungal DNA. https://doi.org/10.5281/zenodo.10435615 (2024). The datafile allData.RData is provided also here for convenience, and it includes the following three objects: metadata, taxonomy, and otu.table (see Ovaskainen et al. for details). The script pipeline consists of the following elements (for deltails, see the Methods of the paper):</p> <ul> <li>Scripts S01: data preparation<br> <ul> <li>S01.1_download_clim_data.R. This script downloads daily climatic data for the entire world.</li> <li>S01.2_select_and_preprocess_clim_data.R. This script selects the data relevant for the study locations and preprocesses it.</li> <li>S01.3_add_climatic_data_to_metadata.R. This script adds the preprocessed climatic data to the metadata.</li> <li>S01.4_otu_guild_assignment.R. This script performs the guild assignment to the OTUs. It utilizes the datafiles Fung_LifeStyle_Data.RDS and funguild_db.rds provided here, and it utilizes the taxonomy of ProtaxFungi provided by Ovaskainen et al. Data from: Global Spore Sampling Project: A global standardized dataset of airborne fungal DNA. https://doi.org/10.5281/zenodo.10435615 (2024). Note that while the paper presents analyses and results only for the trait database of Aguilar-Trugueros et al., the scipts repeat the trait analyses also for the FunGuild database. The reason for not showing the results for the FunGuild database in the paper was that the database of Aguilar-Trugueros et al. contains FunGuild as one of the data sources, and that the results were highly coherent between the two databases.</li> <li>S01.5_add_trait_data_to_taxonomy_and_metadata.R. This script adds the guild data and spore size data to taxonomy (taxon-specific traits) as well as to metadata (community-weighted mean traits). It utilizes the datafile Spore_data_12Nov21.RDS provided here. This script can also be used to generated simulated contamination to the OTU table by setting contaminate=TRUE.</li> </ul> </li> <li>Scripts S02: exploratory analyses <ul> <li>S02.1_show_descriptive_statistics.R. This script outputs basic desriptive statistics from the data.</li> <li>S02.2_make_study_design_maps.R. This script plots the study design map shown in the paper.</li> <li>S02.3_compute_site_and_biome_profiles.R. This script computes site_profiles (needed in ordinations) and biome_profiles (needed to create Venn diagrams).</li> <li>S02.4_make_venns.R. This script produces Venn diagrams.</li> </ul> </li> <li>Scripts S03: ordination analyses <ul> <li>S03.1_make_ordination_maps.R. This script makes the ordination analyses.</li> </ul> </li> <li>Scripts S04: univariate analyses <ul> <li>S04.1_conceptualize_univariate_models.R. This script produces a figure that illustrates conceptually the differenent model variants. </li> <li>S04.2_make_univariate_analysis.R. This script implements the univariate analyses.</li> <li>S04.3_show_univariate_results.R. This script summarizes the results of the univariate analyses by producing tables of AIC and R2.</li> <li>S04.4_plot_univariate_results.R. This script plots the univariate models.</li> <li>S04.5_compute_temporal_turnover.R. This script computes site-specific indices of temporal turnover.</li> <li>S04.6_show_temporal_turnover.R. This script generates a plot illustrating temporal turnover.</li> </ul> </li> <li>Scripts S05: Hmsc analyses <ul> <li>S05.1_define_Hmsc_models.R. This script defines the Hmsc models. It utilizes the R-function as.phylo.formula provided here.</li> <li>S05.2_export_Hmsc_models_for_fitting.R. This script exports the unfitted Hmsc-models for fitting with Hmsc-HPC that operates on python/tensorflow.</li> <li>S05.3_import_fitted_Hmsc_models.R. This script imports the fitted Hmsc-models back to Hmsc-R.</li> <li>S05.4_postprocess_Hmsc_results.R. This script postprocesses the results of the fitted Hmsc model.</li> <li>S05.5_show_Hmsc_results.R. This script generates a plot that illustrates the postprocessed results.</li> </ul> </li> </ul>
MetaComNet: A random forest-based framework for making spatial prediction of plant-pollinator interactions
<p>1. Predicting plant-pollinator interaction networks over space and time will improve our understanding of how environmental change is likely to impact the functioning of ecosystems. Here we propose a framework for producing spatially explicit predictions of the occurrence and number of pairwise plant-pollinator interactions and of the species richness, diversity, and abundance of pollinators visiting flowers. We call the framework 'MetaComNet' because it aims to link metacommunity dynamics to the assembly of ecological networks.</p> <p>2. To illustrate the MetaComNet functionality, we used a dataset on bee-flower networks sampled at 16 sites in southeast Norway along with random forest models to predict bee-flower interactions. We included variables associated with climatic conditions (elevation) and habitat availability within a 250m radius of each site. Regional commonness, site-specific distance to conspecifics, social guild, and floral preference were included as bee traits. Each plant species was assigned a score reflecting its site-specific abundance, and four scores reflecting the bee species that the plant family is known to attract. We used leave-one-out cross-validations to assess the models' ability to predict pairwise plant-bee interactions across the landscape.</p> <p>3. The relationship between observed occurrence or absence of interactions and the predicted probability of interactions was nearly proportional (GLMlogistic regression slope = 1.09), matching the data well (AUC = 0.88), and explained 30% of the variation. Predicted probability of interactions was also correlated with the number of observed pairwise interactions (r = 0.32). The sum of predicted probabilities of bee-flower interactions were positively correlated with observed species richness (r = 0.50), diversity (r = 0.48), and abundance (r = 0.42) of wild bees interacting with plant species within sites.</p> <p>4. Our findings show that the MetaComNet framework can be a useful approach for making spatially explicit predictions and mapping plant-pollinator interactions. Such predictions have the potential to identify areas where the pollination potential for wild plants is particularly high, and where conservation action should be directed to preserve this ecosystem function.</p>
Mimulus cardinalis plasticity analyses and R scripts for: Spatial variation in high temperature-regulated gene expression predicts evolution of plasticity with climate change in the scarlet monkeyflower
<p>A major way that organisms can adapt to changing environmental conditions is by evolving increased or decreased phenotypic plasticity. In the face of current global warming, more attention is being paid to the role of plasticity in maintaining fitness as abiotic conditions change over time. However, given that temporal data can be challenging to acquire, a major question is whether evolution in plasticity across space can predict adaptive plasticity across time. In growth chambers simulating two thermal regimes, we generated transcriptome data for western North American scarlet monkeyflowers (<i>Mimulus cardinalis</i>) collected from different latitudes and years (2010 and 2017) to test hypotheses about how plasticity in gene expression is responding to increases in temperature, and if this pattern is consistent across time and space. Supporting the genetic compensation hypothesis, individuals whose progenitors were collected from the warmer-origin northern 2017 descendant cohort showed lower thermal plasticity in gene expression than their cooler-origin northern 2010 ancestors. This was largely due to a change in response at the warmer (40ºC) rather than cooler (20ºC) treatment. A similar pattern of reduced plasticity, largely due to a change in response at 40ºC, was also found for the cooler-origin northern versus the warmer-origin southern population from 2017. Our results demonstrate that reduced phenotypic plasticity can evolve with warming and that spatial and temporal changes in plasticity predict one another.</p>
Leaf area predicts conspecific spatial aggregation of woody species
<p><strong>Aim:</strong> Addressing how woody plant species are distributed in space can reveal inconspicuous drivers that structure plant communities. The spatial structure of conspecifics varies not only at local scales across co-existing plant species but also at larger biogeographical scales with climatic parameters and habitat properties. The possibility that biogeographical drivers shape the spatial structure of plants, however, has not received sufficient attention.</p> <p><strong>Location:</strong> Global synthesis.</p> <p><strong>Time period:</strong> 1997 - 2022.</p> <p><strong>Major taxa studied:</strong> Woody angiosperms and conifers.</p> <p><strong>Methods:</strong> We carried out a quantitative synthesis to capture the interplay between local scale and larger scale drivers. We modelled conspecific spatial aggregation as a binary response through logistic models and Ripley's L statistics and the distance at which the point process was least random with mixed effects linear models. Our predictors covered a range of plant traits, climatic predictors and descriptors of the habitat.</p> <p><strong>Results:</strong> We hypothesized that plant traits, when summarized by local scale predictors, exceed in importance biogeographical drivers in determining the spatial structure of conspecifics across woody systems. This was only the case in relation to the frequency with which we observe aggregated distributions. The probability of observing spatial aggregation and the intensity of it was higher for plant species with large leaves but further depended on climatic parameters and mycorrhiza.</p> <p><strong>Main Conclusions:</strong> Compared to climatic variables, plant traits perform poorly in explaining the spatial structure of woody plant species, even though leaf area is a decisive plant trait that is related to whether we observe homogenous spatial aggregation and its intensity. Despite the limited variance explained by our models, we found that the spatial structure of woody plants is subject to consistent biogeographical constraints and that these exceed beyond descriptors of individual species, which we captured here through leaf area.</p>
Supplementary files: Machine Learning Insights into Türkiye's Climate Variability: Predictive Modelling and Spatial Analysis
<p>This dataset and python code were used in the study titled "Machine Learning Insights into Türkiye's Climate Variability: Predictive Modelling and Spatial Analysis".</p>
Including a spatial predictive process in band recovery models improves inference for Lincoln estimates of animal abundance
<p>Abundance estimation is a critical component of conservation planning, particularly for exploited species where managers set regulations to restrict harvest based on current population size. An increasingly common approach for abundance estimation is through integrated population modeling (IPM), which uses multiple data sources in a joint likelihood to estimate abundance and additional demographic parameters. Lincoln estimators are one commonly used IPM component for harvested species, which combine information on the rate and the total number of individuals harvested within an integrated band-recovery framework to estimate abundance at large scales.</p> <p>A major assumption of the Lincoln estimator is that banding and recoveries are representative of the whole population, which may be violated if major sources of spatial heterogeneity in survival or harvest rates are not incorporated into the model. We developed an approach to account for spatial variation in harvest rates using a spatial predictive process, which we incorporated into a Lincoln estimator IPM.</p> <p>We simulated data under different configurations of sample sizes, harvest rates, and sources of spatial heterogeneity in harvest rate to assess potential model bias in parameter estimates. We then applied the model to data collected from a field study of wild turkeys (<em>Meleagris gallapavo</em>) to estimate local and statewide abundance in Maine, USA.</p> <p>We found that the band recovery model that incorporated a spatial predictive process consistently provided estimates of adult and juvenile abundance with low bias across a variety of spatial configurations of harvest rate and sampling intensities. When applied to data collected on wild turkeys, a model that did not incorporate spatial heterogeneity underestimated the harvest rate in some sub-regions. Consistent with simulation results, this led to over-estimation of both local and statewide abundance.</p> <p>Our work demonstrates that a spatial predictive process is a viable mechanism to account for spatial variation in harvest rates and limit bias in abundance estimates. This approach could be extended to large-scale band recovery datasets and has applicability for the estimation of population parameters in other ecological models as well.</p>
Predicted Spatially Complete Zoning Map of North Carolina
<p>Spatially-complete zoning map of North Carolina, USA. The <strong>results </strong>folder contains results of a machine learning (random forest) model predicting 3 core district zones (residential, non-residential, and mixed use) and 13 sub-district zones (open space, industrial, commercial, office, planned use, high-density residential, medium-high-density residential, medium-density residential, medium-low-density residential, low-density residential, agricultural residential, mixed use, and downtown). Results are provided as 30-m rasters (.tif) with each value corresponding to a zoning district. Table containing zone district ID (number) and zone district name (character string) is included in <strong>zone_classification.csv</strong>. Final (spatially complete statewide maps) can be found in the <strong>final_predicted </strong>folder. This folder includes Statewide core district results in <strong>NC_predicted_core.tif</strong> and statewide sub-district results in <strong>NC_predicted_sub.tif</strong>. </p> <p>Zoning was generalized and reclassified into 3 core district zones and 13 sub-district zones (described above). Reclassified zoning data, collected from 39 counties in North Carolina is provided in the <strong>observed </strong>folder with core districts in <strong>core_district_observed_zones.tif</strong> and sub-districts in <strong>sub_district_observed_zones.tif</strong>. Also in this folder is <strong>zoning_implementation_NC.csv</strong> which includes links to the source data (zoning map and zoning ordinance) for all collected data.</p> <p>Two models were created to predict zones under different data availability scenarios (i.e., scenarios that assume different levels of data availability). Predictions labeled “within_county” utilized the within-county model which predicts zoning districts in areas where zoning data is partially available for that county. To approximate scenarios of incomplete data accessibility, 20% of the data was randomly removed from training and reserved for independent performance assessments. Predictions labeled “between-county” utilized the between-county model which predicts zoning districts in areas where zoning data is inaccessible. To approximate this scenario, multiple between-county model iterations were computed by randomly removing entire counties from the training dataset and computing performance metrics on the removed (test) counties. Predictions are provided for both core districts and sub-districts (described above). Results from these models can be found in the <strong>predicted </strong>folder. This folder contains four subfolders: <strong>core_district_within_county</strong>, <strong>sub_district_within_county</strong>, <strong>core_district_between_county</strong>, and <strong>sub_district_between_county</strong>. Within each of these folders are predicted maps 30-m raster (.tif), performance reports including precision, recall, and f1 score overall and per district (.csv), and accuracy maps (3-km grid shapefile [.shp, .shx, .prj, .dbf]) with values corresponding to the proportion of misclassified pixels within a grid cell. Multiple randomized testing county samples were conducted for the between-county models. Each random sample is labeled <strong>r*_</strong> where * is replaced with a number between 1 and 15.</p>
Dispersal limitation predicts the spatial and temporal filtering of tropical bird communities in isolated forest fragments
<p>The link between dispersal traits and patterns of community assembly remains a frontier in understanding how vertebrate communities persist in fragmented landscapes. Using experimental release trials and intensive field surveys of bird communities in fragmented forests of the Peruvian and Colombian Andes, we demonstrate that morphological traits related to movement (1) predict experimental flight performance and (2) exhibit dispersal-mediated environmental filtering at the community scale. After correcting for body size, four traits hypothesized to influence flight ability (wing length, wing pointedness, wing loading, and eye size) predicted distance flown across a hostile experimental landscape, with successful species having significantly longer pointed wings, carrying less mass per unit wing area (i.e., lower wing loading), and having smaller eyes. Species with larger eyes also displayed increased flight latency, potentially due to disability glare. At the community scale we detected a gradient of dispersal-mediated environmental filtering in fragments compared to reference forest within the same landscape, with relative differences in trait values explained by the temporal and spatial extent of patch isolation. In the Colombian landscape where fragments had been isolated for > 60 years, communities were filtered for species with long and narrow wings and small eyes, especially within the most spatially isolated fragments. We observed the opposite pattern in the more recently fragmented Peruvian landscape (15-30 years): communities within fragments tended to have shorter and more rounded wings compared to those in nearby contiguous forests, suggesting that dispersal-limited species accumulate in the initial years following patch isolation due to "restricted dispersal" and represent an extinction debt yet to be paid. Our results (1) experimentally validate the use of morphological traits as proxies for movement ability in fragmented landscapes, (2) demonstrate that visual acuity functions as a novel dimension of dispersal limitation, and (3) quantify how the spatial and temporal components of patch isolation produce a gradient in dispersal-mediated environmental filtering and extinction debt for communities inhabiting fragments.</p>
Data from: Combining thermal and hydric constraints for spatially predicting the activity suitability of Neotropical Leptodactylid frogs
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Habitat suitability modeling to predict the spatial distribution of cold-water coral communities affected by the Deepwater Horizon oil spill
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Leaf area predicts conspecific spatial aggregation of woody species
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Dispersal limitation predicts the spatial and temporal filtering of tropical bird communities in isolated forest fragments
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Including a spatial predictive process in band recovery models improves inference for Lincoln estimates of animal abundance
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Data from: Long wavelength topography of Io and predicted isostatic topography resulting from spatial variation in Io's tidal heating
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Data from: Abiotic proxies for predictive mapping of near-shore benthic assemblages: implications for marine spatial planning
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Microbial associations and spatial proximity predict North American moose (Alces alces) gastrointestinal community composition
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Recent climate change and historical population structure predict spatial patterns of admixture between two host-specialized pine sawfly species
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.